Diabetes mellitus is a chronic metabolic disorder caused by an inability of the pancreas to produce sufficient amounts of the hormone insulin resulting in a decreased ability of the body to metabolize glucose. This failure can lead to excessive glucose in the blood stream, or hyperglycemia. Persistent hyperglycemia alone or in combination with hypoinsulinemia is associated with a variety of serious symptoms and life threatening long term complications. Currently, restoration of endogenous insulin production is not yet possible. As a result, therapy is required to help keep blood glucose concentrations within a normal range. Such glycemic control is achieved by regularly supplying external insulin to the body of the patient to reduce levels of blood glucose.
Considerable advancements have been made in diabetes treatment and therapy by the development of drug delivery devices that relieve the need for the patient to use syringes or drug pens to administer multiple daily injections of insulin. These drug delivery devices allow for the delivery of insulin in a manner that is more comparable to the naturally occurring insulin release by the human pancreas and that can be controlled to follow different standards or individually modified protocols to give the patient more customized glycemic control.
These drug delivery devices can be constructed as implantable devices. Alternatively, the device may be an external device with an infusion set for subcutaneous infusion to the patient via the transcutaneous insertion of a catheter, cannula, or transdermal drug transport, such as through a patch. The external drug delivery devices are mounted on clothing or, more preferably, hidden beneath or inside clothing or mounted on the body, and are generally controlled through a user interface built-in to the device or provided on a separate remote device.
Blood or interstitial glucose monitoring is required to achieve acceptable glycemic control with the devices. For example, delivery of suitable amounts of insulin by the drug delivery device requires that the user frequently, episodically, determines his or her blood glucose level by testing. The level is input into the pump or a controller, after which suitable modification may be calculated to the default or currently in-use insulin delivery protocol (i.e., dosage and timing). Such modification is used to adjust the drug delivery device operation accordingly. Alternatively, or in conjunction with such episodic determinations, continuous glucose monitoring (“CGM”) is used with the drug delivery device and allows for closed-loop control of the insulin being infused into the diabetic patient.
Further, and to allow for closed-loop control, autonomous modulation of drug being delivered to the user is provided by a controller using one or more control algorithms. For example, proportional-integral-derivative algorithms (“PID”) that are reactive to observed glucose levels may be utilized. PID can be tuned based on simple rules of the mathematical models of the metabolic interactions between glucose and insulin in a person. Alternatively, model predictive controllers (“MPC”) may be used. The MPC is advantageous because the MPC proactively considers the near future effects of control changes, and is sometimes subject to constraints in determining the output of the MPC, whereas PID typically involves only past outputs in determining future changes. Constraints can be implemented in the MPC such that a solution in a confined “space”, meaning within imposed delivery limitations, is guaranteed and the system is prevented from exceeding a limit that has been reached.
Known MPCs are described in the following documents: U.S. Pat. No. 7,060,059; U.S. Patent Publication Nos. 2011/0313680 and 2011/0257627; International Publication WO 2012/051344; Percival et al., “Closed-Loop Control and Advisory Mode Evaluation of an Artificial Pancreatic Beta Cell: Use of Proportional-Integral-Derivative Equivalent Model-Based Controllers” J. Diabetes Sci. Technol., Vol. 2, Issue 4, July 2008; Paola Soru et al., “MPC Based Artificial Pancreas, Strategies for Individualization and Meal Compensation,” Annual Reviews in Control 36, p. 118-128 (2012); Cobelli et al., “Artificial Pancreas. Past, Present, Future” Diabetes Vol. 60, November 2011; Magni et al., “Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial” J. Diabetes Sci. Techn., Vol. 3, Issue 5, September 2009; Lee et al., “A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator” J. Diabetes Sci. Techn., Vol. 3, Issue 5, September 2009; Lee et al., “A Closed-Loop Artificial Pancreas based on MPC: Human Friendly Identification and Automatic Meal Disturbance Rejection,” Proceedings of the 17th World Congress, The International Federation of Automatic Control, Seoul Korea Jul. 6-11, 2008; Magni et al., “Model Predictive Control of Type 1 Diabetes: An in Silico Trial” J. Diabetes Sci. Techn., Vol. 1, Issue 6, November 2007; Wang et al., “Automatic Bolus and Adaptive Basal Algorithm for the Artificial Pancreatic β-Cell” Diabetes Techn. Ther., Vol. 12, No. 11, 2010; Percival et al., “Closed-Loop Control of an Artificial Pancreatic β-Cell Using Multi-Parametric Model Predictive Control,” Diabetes Res. 2008; Kovatchev et al., “Control to Range for Diabetes: Functionality and Modular Architecture,” J. Diabetes Sci. Techn., Vol. 3, Issue 5, September 2009; and Atlas et al., “MD-Logic Artificial Pancreas System,” Diabetes Care, Vol. 33, No. 5, May 2010. All articles or documents cited in this application are hereby incorporated by reference into this application as if fully set forth herein.
Glucose control systems conventionally use a measure of insulin-on-board that accounts for all bolus insulin injected without accounting for the difference between insulin injected for meal-related purposes versus that for correction (i.e., glucose concentration-lowering) purposes. In systems that do not have a meal model, two models for insulin-on-board accounting are proposed to improve glucose control: patient-facing insulin-on-board and system-facing insulin-on-board. By “patient-facing insulin-on-board” or “PFIOB” is meant insulin-on-board inclusive of meal-related insulin and correction-related insulin, but generally excluding basal insulin; a well-known value easily understood by patients. By “system-facing insulin-on-board” or “SFIOB” is meant, in a system without a meal model, insulin-on-board that has the potential to lower glucose concentration, i.e., correction-related insulin; this value excludes both meal-related insulin and basal insulin, neither of which are intended to lower glucose concentration. The use of these separate models is problematic in that there is a need to separate meal-related insulin from boluses which may include both meal- and correction-related insulin. The systems solve this problem by the use of accurate therapeutic parameters, such as insulin to carbohydrate ratio and insulin sensitivity factor along with the proper use of a bolus calculator. However, if the system user does not inform the system of meal boluses or correction boluses or omits carbohydrates, blood glucose or both while using the bolus calculator, or increases or decreases the calculated bolus dose without system awareness of the rationale for the increase or decrease, an erroneous increase, reduction or suspension of insulin may occur.
Thus, there is a need in the field to provide a diabetes management system that can utilize a set of rules to overcome this disadvantage.